Introduction
This comprehensive report examines emergency department (ER) operations, focusing on critical key performance indicators (KPIs) that reflect the efficiency and effectiveness of patient care from triage to treatment.It analyzes patient visit metrics to measure overall demand and examines the time from door to doctor, providing insights into initial response times. The report also looks into the duration from doctor to decision, highlighting the speed at which healthcare providers can evaluate and decide on necessary interventions. For more information visit Emergency Room (ER)
While the default Quarto appearance looks great, there are a few tweaks I always apply to elevate the report even further. Rather than manually adding them each time, I bundled everything into a custom format called lumo.
Btw, you can learn how to master Quarto thanks to my online course: Productive R Workflow
This document aims at showcasing how versatile the lumo format is. With a few tweaks, I made it fit the brand of a specific company.
Patients Visits
Analyze patterns in patient visits over time to identify peak hours, seasonal variations, and the overall demand for emergency services.By identifying trends and patterns in patient visits, we aim to optimize the quality of care and improve operational effectiveness.
Key Metrics:
- Total Patient Visits: 12,500
- Average Daily Visits: 139
- Peak Hour: 5 PM – 8 PM
- Percentage of Non-Urgent Visits: 30%
- Percentage of Urgent Visits: 70%
- Average Wait Time (Door to Doctor): 20 minutes
Notice that the code chunks are folded by default. You can adjust this behavior with the code-fold option in the document’s YAML header.
This is a good old scatterplot of the iris dataset. If you want to learn more about how to make graphs in R, visit my R graph gallery!
You can also make a boxplot, but please do not hide the underlying distribution! This is widely explain in my data-to-viz.com project in case you’re not convinced 😋. Check the next tab to get convinced!
See what’s happening now that the individual data points are visible! Would be a shame to miss this.